13,573 research outputs found
Dense-Resolution Network for Point Cloud Classification and Segmentation
Point cloud analysis is attracting attention from Artificial Intelligence
research since it can be widely used in applications such as robotics,
Augmented Reality, self-driving. However, it is always challenging due to
irregularities, unorderedness, and sparsity. In this article, we propose a
novel network named Dense-Resolution Network (DRNet) for point cloud analysis.
Our DRNet is designed to learn local point features from the point cloud in
different resolutions. In order to learn local point groups more effectively,
we present a novel grouping method for local neighborhood searching and an
error-minimizing module for capturing local features. In addition to validating
the network on widely used point cloud segmentation and classification
benchmarks, we also test and visualize the performance of the components.
Comparing with other state-of-the-art methods, our network shows superiority on
ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.Comment: To appear in WACV2021. Codes and models are available at:
https://github.com/ShiQiu0419/DRNe
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and
annotation from ShapeNet 3D object database. The benchmark consists of two
tasks: part-level segmentation of 3D shapes and 3D reconstruction from single
view images. Ten teams have participated in the challenge and the best
performing teams have outperformed state-of-the-art approaches on both tasks. A
few novel deep learning architectures have been proposed on various 3D
representations on both tasks. We report the techniques used by each team and
the corresponding performances. In addition, we summarize the major discoveries
from the reported results and possible trends for the future work in the field
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
High quality perception is essential for autonomous driving (AD) systems. To
reach the accuracy and robustness that are required by such systems, several
types of sensors must be combined. Currently, mostly cameras and laser scanners
(lidar) are deployed to build a representation of the world around the vehicle.
While radar sensors have been used for a long time in the automotive industry,
they are still under-used for AD despite their appealing characteristics
(notably, their ability to measure the relative speed of obstacles and to
operate even in adverse weather conditions). To a large extent, this situation
is due to the relative lack of automotive datasets with real radar signals that
are both raw and annotated. In this work, we introduce CARRADA, a dataset of
synchronized camera and radar recordings with range-angle-Doppler annotations.
We also present a semi-automatic annotation approach, which was used to
annotate the dataset, and a radar semantic segmentation baseline, which we
evaluate on several metrics. Both our code and dataset are available online.Comment: 8 pages, 5 figues. Accepted at ICPR 2020. Erratum: results in Table
III have been updated since the ICPR proceedings, models are selected using
the PP metric instead of the previously used PR metri
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
Semantic segmentation was seen as a challenging computer vision problem few
years ago. Due to recent advancements in deep learning, relatively accurate
solutions are now possible for its use in automated driving. In this paper, the
semantic segmentation problem is explored from the perspective of automated
driving. Most of the current semantic segmentation algorithms are designed for
generic images and do not incorporate prior structure and end goal for
automated driving. First, the paper begins with a generic taxonomic survey of
semantic segmentation algorithms and then discusses how it fits in the context
of automated driving. Second, the particular challenges of deploying it into a
safety system which needs high level of accuracy and robustness are listed.
Third, different alternatives instead of using an independent semantic
segmentation module are explored. Finally, an empirical evaluation of various
semantic segmentation architectures was performed on CamVid dataset in terms of
accuracy and speed. This paper is a preliminary shorter version of a more
detailed survey which is work in progress.Comment: To appear in IEEE ITSC 201
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Recently, the advancement of deep learning in discriminative feature learning
from 3D LiDAR data has led to rapid development in the field of autonomous
driving. However, automated processing uneven, unstructured, noisy, and massive
3D point clouds is a challenging and tedious task. In this paper, we provide a
systematic review of existing compelling deep learning architectures applied in
LiDAR point clouds, detailing for specific tasks in autonomous driving such as
segmentation, detection, and classification. Although several published
research papers focus on specific topics in computer vision for autonomous
vehicles, to date, no general survey on deep learning applied in LiDAR point
clouds for autonomous vehicles exists. Thus, the goal of this paper is to
narrow the gap in this topic. More than 140 key contributions in the recent
five years are summarized in this survey, including the milestone 3D deep
architectures, the remarkable deep learning applications in 3D semantic
segmentation, object detection, and classification; specific datasets,
evaluation metrics, and the state of the art performance. Finally, we conclude
the remaining challenges and future researches.Comment: 21 pages, submitted to IEEE Transactions on Neural Networks and
Learning System
Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees
The purpose of this study was to investigate the use of deep learning for
coniferous/deciduous classification of individual trees from airborne LiDAR
data. To enable efficient processing by a deep convolutional neural network
(CNN), we designed two discrete representations using leaf-off and leaf-on
LiDAR data: a digital surface model with four channels (DSMx4) and a set of
four 2D views (4x2D). A training dataset of labeled tree crowns was generated
via segmentation of tree crowns, followed by co-registration with field data.
Potential mislabels due to GPS error or tree leaning were corrected using a
statistical ensemble filtering procedure. Because the training data was heavily
unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced
sub-samples of augmented data (180 rotational variations per instance). The
4x2D representation yielded similar classification accuracies to the DSMx4
representation (~82% coniferous and ~90% deciduous) while converging faster.
The data augmentation improved the classification accuracies, but more real
training instances (especially coniferous) likely results in much stronger
improvements. Leaf-off LiDAR data were the primary source of useful
information, which is likely due to the perennial nature of coniferous foliage.
LiDAR intensity values also proved to be useful, but normalization yielded no
significant improvements. Lastly, the classification accuracies of overstory
trees (~90%) were more balanced than those of understory trees (~90% deciduous
and ~65% coniferous), which is likely due to the incomplete capture of
understory tree crowns via airborne LiDAR. Automatic derivation of optimal
features via deep learning provide the opportunity for remarkable improvements
in prediction tasks where captured data are not friendly to human visual system
- likely yielding sub-optimal human-designed features.Comment: Under review as of the date of submissio
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Semantic scene understanding is important for various applications. In
particular, self-driving cars need a fine-grained understanding of the surfaces
and objects in their vicinity. Light detection and ranging (LiDAR) provides
precise geometric information about the environment and is thus a part of the
sensor suites of almost all self-driving cars. Despite the relevance of
semantic scene understanding for this application, there is a lack of a large
dataset for this task which is based on an automotive LiDAR.
In this paper, we introduce a large dataset to propel research on laser-based
semantic segmentation. We annotated all sequences of the KITTI Vision Odometry
Benchmark and provide dense point-wise annotations for the complete
field-of-view of the employed automotive LiDAR. We propose three benchmark
tasks based on this dataset: (i) semantic segmentation of point clouds using a
single scan, (ii) semantic segmentation using multiple past scans, and (iii)
semantic scene completion, which requires to anticipate the semantic scene in
the future. We provide baseline experiments and show that there is a need for
more sophisticated models to efficiently tackle these tasks. Our dataset opens
the door for the development of more advanced methods, but also provides
plentiful data to investigate new research directions.Comment: ICCV2019. See teaser video at http://bit.ly/SemanticKITTI-tease
A Review on Deep Learning Techniques Applied to Semantic Segmentation
Image semantic segmentation is more and more being of interest for computer
vision and machine learning researchers. Many applications on the rise need
accurate and efficient segmentation mechanisms: autonomous driving, indoor
navigation, and even virtual or augmented reality systems to name a few. This
demand coincides with the rise of deep learning approaches in almost every
field or application target related to computer vision, including semantic
segmentation or scene understanding. This paper provides a review on deep
learning methods for semantic segmentation applied to various application
areas. Firstly, we describe the terminology of this field as well as mandatory
background concepts. Next, the main datasets and challenges are exposed to help
researchers decide which are the ones that best suit their needs and their
targets. Then, existing methods are reviewed, highlighting their contributions
and their significance in the field. Finally, quantitative results are given
for the described methods and the datasets in which they were evaluated,
following up with a discussion of the results. At last, we point out a set of
promising future works and draw our own conclusions about the state of the art
of semantic segmentation using deep learning techniques.Comment: Submitted to TPAMI on Apr. 22, 201
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D
shape analysis. Built upon the octree representation of 3D shapes, our method
takes the average normal vectors of a 3D model sampled in the finest leaf
octants as input and performs 3D CNN operations on the octants occupied by the
3D shape surface. We design a novel octree data structure to efficiently store
the octant information and CNN features into the graphics memory and execute
the entire O-CNN training and evaluation on the GPU. O-CNN supports various CNN
structures and works for 3D shapes in different representations. By restraining
the computations on the octants occupied by 3D surfaces, the memory and
computational costs of the O-CNN grow quadratically as the depth of the octree
increases, which makes the 3D CNN feasible for high-resolution 3D models. We
compare the performance of the O-CNN with other existing 3D CNN solutions and
demonstrate the efficiency and efficacy of O-CNN in three shape analysis tasks,
including object classification, shape retrieval, and shape segmentation
Drought Stress Classification using 3D Plant Models
Quantification of physiological changes in plants can capture different
drought mechanisms and assist in selection of tolerant varieties in a high
throughput manner. In this context, an accurate 3D model of plant canopy
provides a reliable representation for drought stress characterization in
contrast to using 2D images. In this paper, we propose a novel end-to-end
pipeline including 3D reconstruction, segmentation and feature extraction,
leveraging deep neural networks at various stages, for drought stress study. To
overcome the high degree of self-similarities and self-occlusions in plant
canopy, prior knowledge of leaf shape based on features from deep siamese
network are used to construct an accurate 3D model using structure from motion
on wheat plants. The drought stress is characterized with a deep network based
feature aggregation. We compare the proposed methodology on several
descriptors, and show that the network outperforms conventional methods.Comment: Appears in Workshop on Computer Vision Problems in Plant Phenotyping
(CVPPP), International Conference on Computer Vision (ICCV) 201
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